Although semi- and fully-automated analyzer systems are now routinely used to determine blood platelet counts, it is recognized in the art that current automated platelet determination and quantification methods are still hampered by problems of inaccuracy, lack of precision, and lack of reproducibility. This is particularly evident in the analysis of abnormal blood samples, such as those obtained from individuals or patients afflicted with a number of blood dyscrasias and thrombotic disorders such as thrombocytopenia (a decrease in the number of blood platelets), thrombocytosis, and the like. Several reasons for the difficulties and challenges in controlling accuracy of platelet counts may be attributed to 1) the large dynamic range of the platelet count and size for patients; 2) the small size of platelets; 3) the presence of interfering particles of platelet size in the samples undergoing analysis; and 4) the behavior of platelets upon in vitro aging.
Platelet analysis and quantification can be especially difficult in the case of thrombocytopenic individuals who have reduced or low numbers of platelets in their blood samples. This condition frequently results from treatments and therapies commonly used for cancer patients who have decreased thrombotic tendencies. In addition, individuals afflicted with certain immunologic diseases, particularly autoimmune disease, such as idiopathic thrombocytopenic purpura (ITP), often suffer platelet damage and destruction leading to decreased platelet numbers. Further, individuals suffering from aplastic anemia(s) also have reduced numbers of blood platelets. For example, in samples from thrombocytopenic individuals, platelet numbers are often less than 50,000/.mu.l, compared with platelet numbers in the normal range which are on the order of about 150,000-400,000/.mu.l (J. C Dacie and S. M. Lewis, 1984, Practical Haematology, 6th Edition, Churchill Livingstone, London).
Indeed, the accurate enumeration of platelets became even more important with the advent of widely-available platelet replacement therapies for thrombocytopenic patients. In addition, the development and use of a variety of more sophisticated studies of platelet function, e.g., platelet activation and/or adhesiveness determinations, require accurate and precise platelet counts as an integral part of the laboratory hematology test. Also, the quality assurance of platelet packs prepared for transfusion requires that accurate platelet counting procedures be available and in place (R. K. Wertz and J. A. Koepke, 1977, Am. J. Clin. Path., 68(1):195-201).
Needed in the art are more accurate, precise, and sensitive methods for the detection, discrimination, quantification, and characterization of the parameters of platelets for both normal and abnormal blood samples. In addition, accurate and precise platelet analysis methods that can be performed using whole blood samples obviate the initial preparation of platelet-rich plasma by differential centrifugation or sedimentation techniques that is required by some methods. Such whole-blood platelet analysis methods may be useful for diagnosing unsuspected platelet abnormalities, as well as for monitoring platelet counts and parameters of normal individuals and of patients at the onset of a disorder and during the course of treatment or progression of disease. In addition, methods for use on automated systems that can improve the signal resolution and the discrimination of platelets, especially in cases of low platelet counts, are needed in the art. The present invention as described provides such advantages and improvements to the art.
Two fully automated platelet counting and sizing methods are currently known and used by those in the art. One is the aperture impedance method. Whole-blood platelets in aqueous suspension are detected as they pass through a narrow aperture located between two electrodes, thereby increasing the electrical impedance in the aperture relative to that of the suspending medium in rough proportion to platelet volume. Thus, the platelet pulses provide platelet count and platelet volume. Platelets are distinguished from red blood cells in the aperture impedance method on the basis of their size, since platelets as a group are smaller than red blood cells as a group. In some applications of this method, platelet count and size determinations are refined by mathematical analysis of the shape of platelet size distributions. These distributions are fitted to log-normal curves and the parameters of the fitted curves provide platelet count and size. Although the intent of this treatment is to exclude particle debris and small red blood cells whose presence distorts the log-normal platelet volume distribution, such contaminating particles and cells are not always excluded.
A second fully automated method is the laser light scatter method. In this method, whole-blood platelets in aqueous suspension are detected as they intercept a laser beam, thus causing the incident light to scatter at characteristic angles into paths in which optical detectors are placed. The platelet signal pulses provide volume information as well as counts, since platelet volume is considered to be proportional to scattering intensity. Examples of automated flow cytometry instruments which have been designed and are employed to carry out such light scattering methods are the H.cndot..TM.System instruments (commercially available under the trade designation TECHNICON H.cndot..TM.Systems, e.g., H.cndot..TM.1, H.cndot..TM.2, H.cndot..TM.3, and the like, and sold by the assignee hereof) and the ORTHO ELT-8 (Ortho Diagnostics).
Another fully automated cell counting and sizing method comprises a combination of aperture impedance and light scattering. In this method, cells in aqueous suspension are detected both electrically and optically as they pass through a narrow cylindrical aperture suitable for both electrical and optical measurements. For example, this method has been demonstrated for use in cell counting, sizing and refractive index measurements of isovolumetrically sphered red blood cells (U.S. Pat. No. 5,194,909 to D. H. Tycko). The method described includes an electrical impedance detector and a single optical detector for scattered light. However, it is possible to use a plurality of optical detectors disposed at various angles with respect to the optical axis. In the method that uses an electrical impedance detector and a single optical detector, sphered red blood cells are identified and distinguished from platelets on the basis of size differences, as determined by aperture impedance measurements. The size of the red blood cells is also determined using aperture impedance. Hemoglobin concentration is determined on the basis of both the impedance values and the scattering intensities of the red blood cells.
In the ORTHO ELT-8 system, platelets are distinguished from red blood cells simply by differences in scattering intensity over a single cone angle. In the TECHNICON H.cndot..TM.Systems, platelets are also sized on the basis of scattering intensity over a single cone angle; however, they are distinguished from red blood cells on the basis of their characteristic scattering intensities into a pair of suitably chosen detectors. Although the platelet scattering intensity distribution is log-normal, the second laser light scattering method does not refine counts or sizing by fitting the data obtained using log-normal curves. Particle debris in this method is comprised of signals whose scattering intensities fall below a selected threshold.
As mentioned above, the aperture impedance method distinguishes platelets from red blood cells and particle debris on the basis of particle size, as well as on the basis of the log-normal distribution of platelet sizes. In cases where platelets and other particles are of overlapping size, these distinctions blur, and the best that the method can do is to recognize this failure. Moreover, the light scattering method distinguishes platelets from red blood cells based on two-dimensional boundaries, which may be crossed when red blood cells become small or if they fragment, thus also blurring the distinction between the disparate cell populations. Further, the scatter/impedance method has been demonstrated only for red blood cell analysis and not for platelet analysis.
A third, semi-automated method for platelet discrimination involves a combination of laser light scattering and fluorescence to distinguish platelets from red blood cells and particle debris. Whole-blood platelets in aqueous suspension are labelled with platelet specific-antibodies, such as CD42A. The antibodies, in turn, are bound to fluorophores such as fluorescein isothiocyanate (FITC). The labelled platelets scatter incident light and fluoresce as they pass through a fluorescence flow cytometer, such as a Becton Dickinson FACScan (Becton Dickinson). The platelets and platelet-sized particles are distinguished from red blood cells on the basis of two dimensional scattering patterns (forward scatter and side scatter). These "gated" cells are further classified on the basis of fluorescence intensity; with only platelets displaying significant fluorescence (W. Groner et al., 1994, Blood, No. 10 Supplement, 687a; R. Dickerhof and A. von Ruecker, 1995, Clin. Lab. Hematol., 17:163-172).
Although the last two above-described methods allow the discrimination of platelets from other blood cell types and from debris, they do not provide absolute platelet counts. Furthermore, they do not determine platelet size, since there is no simple way to calibrate the methods and the systems performing the methods for this purpose. In addition, the labelling technique is labor-intensive and relatively expensive.
In addition to discriminating and quantifying platelets in blood samples, simple, inexpensive, accurate and reproducible methods for determining platelet activation (or activation state) are needed in the art. The activation state of platelets is an important parameter of platelet function as described below.
Platelet activation is a fundamental functional property of platelets, since activated platelets play an integral role in hemostasis and thrombus. When vascular injury occurs, subendothelial surfaces are exposed at the site of injury, which results in the adhesion of activated platelets to the subendothelial surface. This is followed by platelet granule release, platelet aggregation and thrombus formation. Thrombi are composed of fibrin, platelet aggregates and red blood cells.
Activated platelets are distinct from resting platelets in that the former express surface glycoproteins associated with the adhesion process. Also, activated platelets release granular components and undergo such processes as the disk-to-sphere shape change and aggregation. Swelling is also associated with the shape change.
Thrombosis is part of the normal response to vascular injury. However, increased thrombotic activity also occurs, with negative effects, in conditions such as peripheral vascular disease (D. V. Devine et al., 1993, Arteriosclerosis and Thrombosis, 13:857-62), cardiac ischemia (D. McTavish et al., 1990, Drugs, 40:238; G. DiMinno et al., 1985, J. Clin. Invest., 75:328), diabetes mellitus (D. Tschoepe et al., 1991, Seminars in Thrombosis and Hemostasis, 17:433-438) and angina (R. C. Becker et al., 1994, Coronary Artery Dis., 5:339). It is also known that blood-banked platelets in concentrates become activated during storage and, as a result, lose some of their potency (H. M. Rinder and E. L. Snyder, 1992, Blood Cells, 18:445). In addition, hemodialysis and surgical procedures involving extracorporeal circulation of blood are known to cause platelet activation (e.g., J. C. Reverter et al., 1994, J. Lab. Clin. Med., 124:79; Y. T. Wachtfogel et al., 1993, J. Thoracic and Cardiovascular Surg., 106:1-10; R. E. Scharf et al., 1992, Arteriosclerosis and Thrombosis, 12:1475-1487). Accordingly, the ability to identify and monitor the activation state of platelets ex vivo provides an advantageous and useful screening technique afforded by the present invention.
Platelet activation has been studied using fluorescence flow cytometry (e.g., S. J. Shattil et al., 1987, Blood, 70:307; C. S. Abrams et al., 1990, Blood, 75:128; L. Corash, 1990, Blood Cells, 16:97-108). Using fluorescence technology, platelets are marked with fluorescence-conjugated antibodies specific to glycoproteins that are expressed, or that undergo conformational changes, on the platelet surface as a result of platelet activation. The number of fluorescence-positive events counted on a flow cytometer represents the number of activated platelets; the fluorescence intensity per event represents the number of marked sites per cell surface. Although this technique is specific and sensitive, it is also disadvantageous in several ways, namely, it is expensive; sample preparation is time-consuming; and data analysis is not automated. Further, no standard method has been established for setting fluorescence-positive thresholds, partly because of the arbitrary nature of the threshold position and partly because of differences in experimental design.
Platelet activation has also been studied by density-gradient analysis (B. van Oost et al., 1983, Blood, 62:433-38). The density of platelets drops as they are activated, primarily due to swelling and secondarily due to the release of alpha- and dense-granules (S. Holme et al., 1981, J. Lab. Clin. Med., 97:610-22; S. Holme et al., 1988, J. Lab. Clin. Med., 112:223-231) which are denser than the platelet cytoplasm. Consequently, activated platelet samples have higher percentages of low-density platelets in density-gradient separations than do non-activated samples. The density-gradient separation technique is time consuming and requires a skilled technologist. Further, a cell counter is required to determine the number of platelets in each of the density-gradient fractions.
Accordingly, the present invention which offers a novel, inexpensive and sensitive light scattering, including light scattering/aperture impedance, technique for the determination of platelet activation provides an advancement and advantage to the art. The present method of determining platelet activation is automated and thus is efficient and time-saving for clinical use.